The 2011 Report on Wedding Dresses: World Market Segmentation by City
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More information from http://www.researchandmarkets.com/reports/1602640/
The 2011 Report on Wedding Dresses: World Market Segmentation by City
Description: Market Potential Estimation Methodology
Overview
This study covers the world outlook for wedding dresses across more than 2000 cities. For the year
reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the city in
question (in millions of U.S. dollars), the percent share the city is of the region and of the globe. These
comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. Using econometric models
which project fundamental economic dynamics within each country and across countries, latent demand
estimates are created. This report does not discuss the specific players in the market serving the latent
demand, nor specific details at the product level. The study also does not consider short-term cyclicalities
that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run
view, irrespective of the players or products involved.
This study does not report actual sales data (which are simply unavailable, in a comparable or consistent
manner in virtually all of the cities of the world). This study gives, however, my estimates for the worldwide
latent demand, or the P.I.E. for wedding dresses. It also shows how the P.I.E. is divided across the world’s
cities. In order to make these estimates, a multi-stage methodology was employed that is often taught in
courses on international strategic planning at graduate schools of business.
What is Latent Demand and the P.I.E.?
The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant,
not observable, or not yet realized. Demand is the notion of an economic quantity that a target population
or market requires under different assumptions of price, quality, and distribution, among other factors.
Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when
that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of
potential industry earnings (P.I.E.) or total revenues (not profit) if a market is served in an efficient manner. It
is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given
level in the value chain. There can be latent demand at the retail level, at the wholesale level, the
manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always
smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain
minimum profitability).
The latent demand for wedding dresses is not actual or historic sales. Nor is latent demand future sales. In
fact, latent demand can be lower either lower or higher than actual sales if a market is inefficient (i.e., not
representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the
lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the
part of firms. In general, however, latent demand is typically larger than actual sales in a city market.
Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures
assume the long-run efficiency of currency markets. Figures, therefore, equate values based on purchasing
power parities across countries. Short-run distortions in the value of the dollar, therefore, do not figure into
the estimates. Purchasing power parity estimates of country income were collected from official sources,
and extrapolated using standard econometric models. The report uses the dollar as the currency of
comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln.
For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent
revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in
this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends) and not
adjusted for future dynamics in exchange rates (i.e., the figures reflect average exchange rates over recent
history). If inflation rates or exchange rates vary in a substantial way compared to recent experience,
actually sales can also exceed latent demand (when expressed in U.S. dollars, not adjusted for inflation). On
the other hand, latent demand can be typically higher than actual sales as there are often distribution
inefficiencies that reduce actual sales below the level of latent demand.
As mentioned earlier, this study is strategic in nature, taking an aggregate and long-run view, irrespective of
the players or products involved. If fact, all the current products or services on the market can cease to exist
in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can bereplaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be an international latent demand for wedding dresses at the aggregate level. Product and service offering details, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand. The Methodology In order to estimate the latent demand for wedding dresses on a city-by-city basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium in realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question. Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for wedding dresses across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for wedding dresses. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different
propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences). Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for wedding dresses. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just wedding dresses. Step 1. Product Definition and Data Collection Any study of latent demand across countries requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries or cities are more likely to be at or near efficiency than others. These are given greater weight than others in the estimation of latent demand compared to others for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets). The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany. Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “wedding dresses” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within wedding dresses falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all cities and the world at large (without needing to know the specific parts that went into the whole in the first place). The wedding dress industry includes formal gowns of all materials which are generally light in color and designed to be worn during a wedding ceremony. Companies participating in this industry include Casablanca, David's Bridal, and Watters & Watters. Step 2. Filtering and Smoothing Based on the aggregate view of wedding dresses as defined above, data were then collected for as many similar countries and cities as possible for that same definition, at the same level of the value chain. This generates a convenience sample from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis. Step 3. Filling in Missing Values In some cases, data are available for countries or cities on a sporadic basis. In other cases, data may be
available for only one year. From a Bayesian perspective, these observations should be given greatest
weight in estimating missing years. Assuming that other factors are held constant, the missing years are
extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of
a long-run consumption function (defined earlier), cities which have missing data for any given year, are
estimated based on historical dynamics of aggregate income for that country.
Step 4. Varying Parameter, Non-linear Estimation
Given the data available from the first three steps, the latent demand is estimated using a “varying-
parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand
is assumed to be constant across cities unless there is empirical evidence to suggest that this effect varies
(i.e., the slope of the income effect is not necessarily same for all countries). This assumption applies across
cities along the aggregate consumption function, but also over time (i.e., not all cities are perceived to have
the same income growth prospects over time and this effect can vary from city to city as well). Another way
of looking at this is to say that latent demand for wedding dresses is more likely to be similar across cities
that have similar characteristics in terms of economic development (i.e., African cities will have similar latent
demand structures controlling for the income variation across the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate
consumption function. For some categories, however, the reader must realize that the numbers will reflect a
city’s contribution to global latent demand and may never be realized in the form of local sales. For certain
category combinations this will result in what at first glance will be odd results. For example, the latent
demand for the category “space vehicles” will exist for cities in “Togo” even though they have no space
program. The assumption is that if the economies in these countries did not exist, the world aggregate for
these categories would be lower. The share attributed to these cities is based on a proportion of their
income (however small) being used to consume the category in question (i.e., perhaps via resellers).
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function.
Because the world consists of more than 2000 cities, there will always be those cities, especially toward the
bottom of the consumption function, where non-linear estimation is simply not possible. For these cities,
equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s
stock of income), but a function of current income (a city’s flow of income). In the long run, if a city has no
current income, the latent demand for wedding dresses is assumed to approach zero. The assumption is
that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income
will not use their savings, in the long run, to demand wedding dresses). In a graphical sense, for low income
cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage
of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their
income, based on the city closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all cities of the world,
including for the smallest economies. These are then aggregated to get world totals and regional totals. To
make the numbers more meaningful, regional and global demand averages are presented. Figures are
rounded, so minor inconsistencies may exist across tables.
Contents: 1 INTRODUCTION & METHODOLOGY
1.1 Overview and Definitions
1.2 Market Potential Estimation Methodology
1.2.1 Overview
1.2.2 What is Latent Demand and the P.I.E.?
1.2.3 The Methodology
1.2.3.1 Step 1. Product Definition and Data Collection
1.2.3.2 Step 2. Filtering and Smoothing
1.2.3.3 Step 3. Filling in Missing Values
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation
1.2.3.6 Step 6. Aggregation and Benchmarking
2 USING THE DATA
3 CITY SEGMENTS RANKED BY MARKET SIZE
3.1 Top 15 Markets
3.2 Markets 16 to 30
3.3 Remaining Cities by Market Rank4 CITY SEGMENTS IN ALPHABETICAL ORDER 4.1 A: from Aalborg to Az Zawiyah 4.2 B: from Bacolod to Bydgoszcz 4.3 C: from Caaguazu to Cyangugu 4.4 D: from Da Nang to Dzhizak 4.5 E: from East London to Esteli 4.6 F: from Fagatogo to Funchal 4.7 G: from Gabes to Gyumri 4.8 H: from Hachinohe to Hyderabad 4.9 I: from Iasi to Izmir 4.10 J: from Jaboatao to Jyvaskyla 4.11 K: from Kabul to Kzyl-Orda 4.12 L: from La Ceiba to Lyon 4.13 M: from Macae to Mzuzu 4.14 N: from Nacala to Nzerekore 4.15 O: from Oaklahoma City to Oyem 4.16 Ö: from Örebro to Örebro 4.17 P: from Pago Pago to Pyuthan 4.18 Q: from Qandahar to Quito 4.19 R: from Rabat to Rustavi 4.20 S: from S. Luis Potosi to Szombathely 4.21 T: from Tabligbo to Tyre 4.22 U: from Uberaba to Utulei 4.23 V: from Vacoas-Phoenix to Vukovar 4.24 W: from Wadi Medani to Wuhan 4.25 X: from Xalapa to Xi'an 4.26 Y: from Yamagata to Yungkang 4.27 Z: from Zadar to Zvishavane 5 CITY SEGMENTS RANKED BY COUNTRY 5.1 Afghanistan 5.2 Albania 5.3 Algeria 5.4 American Samoa 5.5 Andorra 5.6 Angola 5.7 Antigua and Barbuda 5.8 Argentina 5.9 Armenia 5.10 Aruba 5.11 Australia 5.12 Austria 5.13 Azerbaijan 5.14 Bahrain 5.15 Bangladesh 5.16 Barbados 5.17 Belarus 5.18 Belgium 5.19 Belize 5.20 Benin 5.21 Bermuda 5.22 Bhutan 5.23 Bolivia 5.24 Bosnia and Herzegovina 5.25 Botswana 5.26 Brazil 5.27 Brunei 5.28 Bulgaria 5.29 Burkina Faso 5.30 Burma 5.31 Burundi 5.32 Cambodia 5.33 Cameroon 5.34 Canada
5.35 Cape Verde 5.36 Central African Republic 5.37 Chad 5.38 Chile 5.39 China 5.40 Christmas Island 5.41 Colombia 5.42 Comoros 5.43 Congo (formerly Zaire) 5.44 Cook Islands 5.45 Costa Rica 5.46 Cote d'Ivoire 5.47 Croatia 5.48 Cuba 5.49 Cyprus 5.50 Czech Republic 5.51 Denmark 5.52 Djibouti 5.53 Dominica 5.54 Dominican Republic 5.55 Ecuador 5.56 Egypt 5.57 El Salvador 5.58 Equatorial Guinea 5.59 Estonia 5.60 Ethiopia 5.61 Fiji 5.62 Finland 5.63 France 5.64 French Guiana 5.65 French Polynesia 5.66 Gabon 5.67 Georgia 5.68 Germany 5.69 Ghana 5.70 Greece 5.71 Greenland 5.72 Grenada 5.73 Guadeloupe 5.74 Guam 5.75 Guatemala 5.76 Guinea 5.77 Guinea-Bissau 5.78 Guyana 5.79 Haiti 5.80 Honduras 5.81 Hong Kong 5.82 Hungary 5.83 Iceland 5.84 India 5.85 Indonesia 5.86 Iran 5.87 Iraq 5.88 Ireland 5.89 Israel 5.90 Italy 5.91 Jamaica 5.92 Japan 5.93 Jordan 5.94 Kazakhstan 5.95 Kenya 5.96 Kiribati 5.97 Kuwait
5.98 Kyrgyzstan 5.99 Laos 5.100 Latvia 5.101 Lebanon 5.102 Lesotho 5.103 Liberia 5.104 Libya 5.105 Liechtenstein 5.106 Lithuania 5.107 Luxembourg 5.108 Macau 5.109 Madagascar 5.110 Malawi 5.111 Malaysia 5.112 Maldives 5.113 Mali 5.114 Malta 5.115 Marshall Islands 5.116 Martinique 5.117 Mauritania 5.118 Mauritius 5.119 Mexico 5.120 Micronesia Federation 5.121 Moldova 5.122 Monaco 5.123 Mongolia 5.124 Morocco 5.125 Mozambique 5.126 Namibia 5.127 Nauru 5.128 Nepal 5.129 New Caledonia 5.130 New Zealand 5.131 Nicaragua 5.132 Niger 5.133 Nigeria 5.134 Niue 5.135 Norfolk Island 5.136 North Korea 5.137 Norway 5.138 Oman 5.139 Pakistan 5.140 Palau 5.141 Palestine 5.142 Panama 5.143 Papua New Guinea 5.144 Paraguay 5.145 Peru 5.146 Philippines 5.147 Poland 5.148 Portugal 5.149 Puerto Rico 5.150 Qatar 5.151 Republic of Congo 5.152 Reunion 5.153 Romania 5.154 Russia 5.155 Rwanda 5.156 San Marino 5.157 Sao Tome E Principe 5.158 Saudi Arabia 5.159 Senegal 5.160 Seychelles
5.161 Sierra Leone
5.162 Singapore
5.163 Slovakia
5.164 Slovenia
5.165 Solomon Islands
5.166 Somalia
5.167 South Africa
5.168 South Korea
5.169 Spain
5.170 Sri Lanka
5.171 St. Kitts and Nevis
5.172 St. Lucia
5.173 St. Vincent and the Grenadines
5.174 Sudan
5.175 Suriname
5.176 Swaziland
5.177 Sweden
5.178 Switzerland
5.179 Syrian Arab Republic
5.180 Taiwan
5.181 Tajikistan
5.182 Tanzania
5.183 Thailand
5.184 The Bahamas
5.185 The British Virgin Islands
5.186 The Cayman Islands
5.187 The Falkland Islands
5.188 The Gambia
5.189 The Netherlands
5.190 The Netherlands Antilles
5.191 The Northern Mariana Island
5.192 The U.S. Virgin Islands
5.193 The United Arab Emirates
5.194 The United Kingdom
5.195 The United States
5.196 Togo
5.197 Tokelau
5.198 Tonga
5.199 Trinidad and Tobago
5.200 Tunisia
5.201 Turkey
5.202 Turkmenistan
5.203 Tuvalu
5.204 Uganda
5.205 Ukraine
5.206 Uruguay
5.207 Uzbekistan
5.208 Vanuatu
5.209 Venezuela
5.210 Vietnam
5.211 Wallis and Futuna
5.212 Western Sahara
5.213 Western Samoa
5.214 Yemen
5.215 Zambia
5.216 Zimbabwe
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS
6.1 Disclaimers & Safe Harbor
6.2 ICON Group International, Inc. User Agreement Provisions
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